2020机器学习深度学习目录

P1. Machine Learning 2020_ Course Introduction

P2. Rule of ML 2020

P3. Regression - Case Study

P4. Basic Concept

P5. Gradient Descent_1

P6. Gradient Descent_2

P7. Gradient Descent_3

P8. Optimization for Deep Learning 1_2 选学

P9. Optimization for Deep Learning 2_2 选学

P10. Classification_1

P11. Logistic Regression

P12. Brief Introduction of Deep Learning

P13. Backpropagation

P14. Tips for Training DNN

P15. Why Deep-

P16. PyTorch Tutorial

P17. Convolutional Neural Network

P18. Graph Neural Network 1_2 选学

P19. Graph Neural Network 2_2 选学

P20. Recurrent Neural Network Part I

P21. Recurrent Neural Network Part II

P22. Unsupervised Learning - Word Embedding

P23. Transformer

P24. Semi-supervised

P25. ELMO, BERT, GPT

P26. Explainable ML 1_8

P27. Explainable ML 2_8

P28. Explainable ML 3_8

P29. Explainable ML 4_8

P30. Explainable ML 5_8

P31. Explainable ML 6_8

P32. Explainable ML 7_8

P33. Explainable ML 8_8

P34. More about Explainable AI 选学

P35. Attack ML Models 1_8

P36. Attack ML Models 2_8

P37. Attack ML Models 3_8

P38. Attack ML Models 4_8

P39. Attack ML Models 5_8

P40. Attack ML Models 6_8

P41. Attack ML Models 7_8

P42. Attack ML Models 8_8

P43. More about Adversarial Attack 1_2 选学

P44. More about Adversarial Attack 2_2 选学

P45. Network Compression 1_6

P46. Network Compression 2_6

P47. Network Compression 3_6

P48. Network Compression 4_6

P49. Network Compression 5_6

P50. Network Compression 6_6

P51. Network Compression 1_2 - Knowledge Distillation .flv

P52. Network Compression 2_2 - Network Pruning 选学

P53. Conditional Generation by RNN & Attention

P54. Pointer Network

P55. Recursive

P56. Transformer and its variant 选学

P57. Unsupervised Learning - Linear Methods

P58. Unsupervised Learning - Neighbor Embedding

P59. Unsupervised Learning - Auto-encoder

P60. Unsupervised Learning - Deep Generative Model Part.flv

P61. Unsupervised Learning - Deep Generative Model Part.flv

P62. More about Auto-encoder 1_4

P63. More about Auto-encoder 2_4

P64. More about Auto-encoder 3_4

P65. More about Auto-encoder 4_4

P66. Self-supervised Learning 选学

P67. Anomaly Detection 1_7

P68. Anomaly Detection 2_7

P69. Anomaly Detection 3_7

P70. Anomaly Detection 4_7

P71. Anomaly Detection 5_7

P72. Anomaly Detection 6_7

P73. Anomaly Detection 7_7

P74. More about Anomaly Detection 选学

P75. Generative Adversarial Network1_10

P76. Generative Adversarial Network2_10

P77. Generative Adversarial Network3_10

P78. Generative Adversarial Network4_10

P79. Generative Adversarial Network5_10

P80. Generative Adversarial Network6_10

P81. Generative Adversarial Network7_10

P82. Generative Adversarial Network8_10

P83. Generative Adversarial Network9_10

P84. Generative Adversarial Network10_10

P85. SAGAN, BigGAN, SinGAN, GauGAN, GANILLA, NICE-GAN（选学.flv

P86. Transfer Learning

P87. More about Domain Adaptation 1_2 选学

P88. More about Domain Adaptation 2_2 选学

P89. Meta Learning – MAML 1_9

P90. Meta Learning – MAML 2_9

P91. Meta Learning – MAML 3_9

P92. Meta Learning – MAML 4_9

P93. Meta Learning – MAML 5_9

P94. Meta Learning – MAML 6_9

P95. Meta Learning – MAML 7_9

P96. Meta Learning – MAML 8_9

P97. Meta Learning – MAML 9_9

P98. More about Meta Learning 选学

P99. More about Meta Learning 选学

P100. Life Long Learning 1_7

P101. Life Long Learning 2_7

P102. Life Long Learning 3_7

P103. Life Long Learning 4_7

P104. Life Long Learning 5_7

P105. Life Long Learning 6_7

P106. Life Long Learning 7_7

P107. Deep Reinforcemen Learning3_1

P108. Deep Reinforcemen Learning3_2

P109. Deep Reinforcemen Learning3_3

P110. RL Advanced Version_1_Policy Gradient

P111. RL Advanced Version_2_ Proximal Policy Optimizatio.flv

P112. RL Advanced Version_3_Q-Learning

P113. RL Advanced Version_4_Q-Learning Advanced Tips

P114. RL Advanced Version_5_Q-Learning Continuous Action.flv

P115. RL Advanced Version_6_Actor-Critic

P116. RL Advanced Version_7_Sparse Reward

P117. RL Advanced Version_8_Imitation Learning